GridTeractions: simulation platform to interact with distribution systems Cristian Zambrano, Cesar Trujillo, David Celeita, Miguel Hernandez and Gustavo Ramos Department of Electrical and Electronic Engineering Universidad de los Andes Bogota D.C., Colombia Email:
[email protected] [email protected] [email protected] [email protected] [email protected] Abstract—This paper describes the design and implementation of GridTeractions, a scalable framework for teaching and testing advanced distribution automation strategies in smart grids. GridTeractions provides a hardware-software platform to interact with online running simulation of distribution systems. The interactive approach leads to fast learning processes for power systems students. Besides, the detailed modeling and continuous results emulate the real systems’ operation. This feature allows realistic feedback for testing and design of future automation strategies. The platform uses network communications with Human-Machine Interfaces developed in microprocessor terminals (Raspberry PI 2) to achieve multi-terminal operation. Systems are modeled with OpenDSS scripts and managed by DSSim-PC in order to run simulations in time series with phase domain results, allowing users to continuously evaluate the electrical variables. This paper includes the architecture description, implementation details and validations based in the IEEE test feeders. Index Terms—Real-time systems, digital simulator, smart grid, simulator.
I. I NTRODUCTION The power system simulation is a fundamental technique in engineering for most design and analysis processes. There are many efforts to improve the usability, precision, and performance for simulators in multiple areas [1]; however, power system engineers are dealing with an increasing level of complexity in the grid, and novel alternatives of simulation which provide a further comprehension of circuits with high variety of devices and modes of operation [2]–[6]. In addition to this challenge, there are plenty of designs for test beds in applications which demand realistic feedback in controlled environments, but these implementations lack flexibility and scalability to explore applications different to the original design. Real-time simulation is a complementary technique that gives the possibility to obtain high fidelity results relying on software solution of models [7]. In this way, the electromagnetic transient simulation of complex systems could be adapted to a wide range of applications [8], [9]. Currently, the real-time simulation relies on high computational power platforms to be capable of simulating complex scenarios, such as distribution systems with thousands of
978-1-5090-4168-8/16/$31.00 ©2016 IEEE
users and automation devices. This characteristic is a major restriction for low-cost applications in which the complexity and realism are also important. In this paper, the authors introduce the GridTeractions platform as an alternative to achieve a realistic simulation of complex distribution systems with educational purposes. The platform’s users interact with the simulation by means of distributed clients with embedded graphical interfaces. Each client is capable of modifying the model parameters; providing feedback with electrical results from the simulation. The main server runs a solver engine with a centralized solution of the power flow. This central interface produces a synchronized temporal simulation and manages the data flow with the user commands and model parameters in each time step. As a result, the user has an instant feedback on the system’s condition with any action performed in client terminals. In summary, the user is capable of running and modifying a simulation with full interaction, without the need for configuring and running independent scenarios of interest. Additionally, data processing algorithms can be implemented in the server, allowing the current architecture to be suitable for testing automated Smart Grid control strategies. As a result of this methodology, users and their automation strategies are included in the simulation loop, in a way that could be named as Human in the Loop (HITL). Recent and future technologies are including a variety of new operational phenomena; certainly, the operation of these components brings new challenges in distribution networks, such as harmonics, bi-directional power flows, the balance of flows on the network, appropriate protection coordination and so forth. To explore these technologies in a distribution network and avoid hidden effects in its operation, the interaction with realistic emulations are needed. The following sections include details about the architecture, implementation, and performance results with the IEEE Test Feeders to illustrate the proposed approach.
NI CRIO 9082
RASPBERRY PI 2
UML CLIENT
SERVER
Power System Simulation DSSim-pc
GRAPHICAL INTERFACE
OPEN DSS
MODEL SOLVER
JAVA – JDK7
CONTROL Terminal
TCP/IP
User’s interface Active and future electrical components
RECEIVE LOOP
SEND LOOP
ETHERNET WINDOWS EMBEDDED
DESIGNED COMMUNICATION PROTOCOL
DISTRIBUTION SYSTEM SIMULATION RUNNING AS A DMS – CONTROL CENTRE
USER OF DYNAMIC ELEMENT FROM THE POWER SYSTEM SIMULATION
Fig. 1. Hardware - software design
II. T HE ARCHITECTURE GridTeractions is a simulator platform for distribution systems that allows the remote and distributed control of electrical components based on a multi-terminal and multiplatform scalable architecture (Fig. 1 - the integration will be explained in the next section). The system architecture has a single server running on a National Instruments CompactRIO cRIO-9082 and multiple clients each one running on a Raspberry PI 2 [10]. The Raspberry PI is an useful device for applications based on client and server communication with multi-terminal architectures [11], [12]. The server and clients communicate through the TCP/IP protocol over LAN, as it is shown in the Fig. 2. For the purpose of this paper, the communication between the main server and one client is explained (highlighted in green).
V/I SOURCES
DGs PVs AND WIND GENERATORS
SWITCHES AND RECLOSERS
manager. The distribution system is simulated in DSSim-PC and communicates with the server via the DSSim connect library for LabVIEW, which internally uses a TCP/IP Protocol. The communication between server and clients is based on a custom protocol designed to satisfy the system needs using the TCP/IP protocol over an Ethernet local network. The detailed hardware and software structure of the server is shown in Fig. 1. The server has three main processes to receive commands and send information to the client, and a final process designed to manage the simulation time-steps.In the final process control strategies may be implemented by processing all the available data from DSSim-PC and the system clients. Each one of the server processes is coded as a state machine. In this way, the implementation of new features or devices is highly scalable. By controlling these states it is possible to define a communication protocol to exchange information with the remote clients.
CAPACITOR BANKS AND REACTORS
B. Clients integration and operation TRs AND REGULATORS
RESIDENTIAL AND COMMERCIAL
EVs, STORAGE AND CONVERTERS
DISTRIBUTION SYSTEM SIMULATION SOFT REAL-TIME
DYNAMIC LOADS
Fig. 2. GridTeractions architecture.
A. Distribution system simulation The platform relies on the OpenDSS software [13] as a distribution system simulator to obtain continuous results of power flow in phase domain. This engine, developed by EPRI, includes a gallery of models with basic devices for the operation of automation strategies in distribution systems [14], such as switches, regulators, meters, etc. GridTeractions server also uses the DSSim-PC software [15] as a solution manager based on OpenDSS in co-simulation with LabVIEW [16]. The server, running on Windows 7 embedded, is implemented in graphical language (LabVIEW), including a TCP
978-1-5090-4168-8/16/$31.00 ©2016 IEEE
Before running the server, it is necessary to configure the simulation parameters in DSSim-PC to define the step time simulation, the number of iterations by step time, and the final time of the simulation. In order to externally control the simulation from the server, it is necessary to configure a Remote Controlled Simulation Time. Then the simulation must be started. As shown in Fig 3, when the server is initialized, it configures the TCP/IP protocol and waits for any incoming request. From the client side the TCP/IP connection is configured by the Java libraries java.net and java.io and uses the IP direction and a known listening port in the cRIO to establish the communication. The first time a Raspberry Pi starts a connection, it sends a Start request message and the server answers with the ID number that will be used to identify the client. The server then sends a list of element types from a defined group (e.g. Capacitor, load, switch and transformer) that are present in the distribution system. This is done by sending an OpenDSS command from the server to DSSim-PC and processing the
received simulation data. The list of available element types arrives to the client and each component is shown in the GUI so that the user can select the type of element that will monitor and control. Then the selected type is sent to the server.
Server Set Communication TCP/IP Receive Request: R
No R = Add
Yes
Yes Send Client ID Receive Process
Ask Available DSSim-PC Types
No
R = Modify
No
Receive Process
R = Start
C. Communication Protocol The communication protocol between Server System and Client System is based on peer to peer communication. The message structure of the communication protocol has a symbol that indicates the start and end of the command message. It is followed by a three digit integer number with the size of the command message. Then is the ID number that identifies the client and it is bounded by a specific symbol. Now comes the information part of the command message. It is separated by a specific symbol that permit differentiate multiple parameter on the same command. So each parameter to be sent will be separated by this symbol. Finally, each parameter has a specific tag that indicates the type of the parameter and a separator symbol for identifying the value of the parameter. An example is shown in Fig.4.
Yes Modify Element Values
Send Type List Receive Selected Type
Send Element Values
Send Elements Names List
Step In Simulation
Receive Selected Element Elements List
Fig. 4. Command example
Message Sending Loop Send and Control Processes
Ask DSSim-PC Elements Names
Read Element Values
*Client ID *Type *Name
Fig. 3. Flow diagram
When the selected element type arrives to the server, it sends back a list of the names and nodes from all the elements in the simulated system that belong to the same type selected by the client. The client then selects an element from the incoming list and creates a software entity with its most important electrical properties. The values of these properties are refreshed with each simulation step and can be monitored and modified from the GUI. The selected element is added to a list in the server with the information of the corresponding client, type and name. Then the server keeps executing two concurrent processes, the first is to send the client all the simulation values of the elements in the list and the second is to listen to new incoming request commands. If the server receives an Add request the process described before is executed to add a new element to an existing client and if the received message is Modify then the object properties in OpenDSS are set to the incoming values.
978-1-5090-4168-8/16/$31.00 ©2016 IEEE
D. UML Design The client system is the one that remote control the properties of one or several elements of the distribution system simulated on DSSim-PC by creating software entities that represent the elements in the simulation. The client receives from the server the properties of each element associated and displays the information on the GUI. The client can modify the active power, reactive power and power factor of a specific element. For the implementation it was necessary to use an architecture based on concurrency. Therefore, two execution threads were implemented: Message Reception and Client Execution.
Fig. 5. UML design
Core is the main class that manages the stored information of the client elements and the communication with the Server System (Fig.5). This class has two attributes: communicator
Fig. 6. User interface: DSSim-PC (IEEE 123 node test feeder) - LabVIEW Distribution system monitor (NI Crio) - GridTeracions client (Raspberry Pi)
and elements. The first one is responsible for the communication with Server System. This class has methods that permit send and receive information through a TCP / IP connection between Client System and Server System. The second one is responsible for the management of client elements list. The associated elements are of type Element class. This class has the most important electrical properties (e.g. Voltages, currents and powers) that the client user can visualize or modify. As all electrical elements in a distribution system share these properties the architecture implements the inheritance mechanism. Therefore, each element inherits the attributes and methods of the superclass Element and implement their own attributes and methods necessary to describe with more details a specific element. MessageReceptionThread is the class responsible for receiving messages from Server System. This class allows that the client user can interact with GUI constantly. The parameters of each element that the user client selects are updated and displayed on the GUI. III. I MPLEMENTATION A. Models of Elements The platform manages four main elements, i.e. capacitors, loads, switches and transformers. These elements can be controlled by the client user from the GUI on Raspberry Pi 2. The client user can choose which elements wants to control and these elements will be displayed in a list on the GUI. Each element has associated the main properties (e.g. Name, type, node, status, three-phase voltages and currents, active power, reactive power and power factor). The client user can modify parameters like the active power, reactive power and power factor. Then immediately the parameters of the element in DSSim-PC are modified and updated to resume the distribution system simulation. Finally, each element has the specific model developed in DSSim-PC. B. Multiple user concurrency Each client connects using a different data socket on a defined port in the server. The commands from the clients are identified by the TCP listener. The server groups the elements from all the clients to request simultaneously the data from DSSim-PC. The values are sent by knowing the ID associated with each element that identifies the corresponding client, and hence the corresponding TCP connection.
978-1-5090-4168-8/16/$31.00 ©2016 IEEE
C. Test systems During the implementation one distribution system is running on DSSim - PC. This power system contains all the elements that the user can choose and control. The distribution systems simulated are some of the IEEE systems that were designed to evaluate and benchmark algorithms in solving unbalanced three-phase radial systems [17]. Therefore the IEEE 13-, 34- and 123-bus Feeder were simulated. DSSimPC provides these distribution systems in the examples folder. These distribution systems have the load curve that allows the user to visualize significant changes electrical properties for all controlled elements. An example of the user’s interfaces is shown in Fig. 6. IV. VALIDATION AND RESULTS ANALYSIS A. Computational performance Raspberry PI 2 The main tasks executed in a Raspberry PI are the GUI control and the continuous update of values for all the associated elements. Due to the need to optimize resources in a Raspberry Pi 2, it is important to determine its performance and resource utilization when managing many elements. This is done by measuring the CPU utilization when assigning different number of elements from the IEEE 123 bus Feeder. The performance analysis is done with the assessment of 4 different cases as shown in Table.I. TABLE I S CENARIOS OF STUDY: IEEE 123
Case 1 Case 2 Case 3 Caso 4
NODE TEST FEEDER
Load
Capacitor
Switch
Transformer
5 10 50 96
4 4 4 4
5 10 12 12
5 8 8 8
In previous cases the maximum CPU utilization is 189.6% for case 3 (Fig. 7a). These peak values in each case are due to the initialization of Java libraries. After that, the first time Java libraries are called in the CPU utilization is approximately established at 40% in the four cases. In the same, way the memory utilization is equally established in equilibrium. After that, Java libraries are called (Fig.7b). B. Computational performance cRIO The main tasks were executed in the cRIO: DSSim-PC running and the server parallel working to continuously update
reconfiguration, and volt/var control are currently in development to be included and tested on the platform. The design allows scalable development with communication protocols and UML clients, so a full interface integrating more raspberry targets is expected. Each client will have an HMI to interact with the power system simulation. Finally, the development of a JAVA library to communicate with DSSim-PC is considered to reduce the system costs by coding the server in JAVA. (a)
(b)
Fig. 7. (a) CPU performance and (b) Memory utilization
the property values of all elements in each Raspberry PI client. In this case the main restriction is the time window in which these tasks must be done in order to avoid data loss. The assessment is done by measuring the time it takes the server to request the property values for an increasing number of elements associated with a single client. As it is shown in the Fig.8, the communication time is high because DSSim-PC can create only one TCP connection at the same time for data exchange with LabVIEW. For this reason, the time increases linearly with more elements and requested properties as there are needed more commands from the server.
Fig. 8. Data request communication time between DSSim-PC and server
V. C ONCLUSION AND FURTHER WORK This paper presented the first version of GridTeractions, a versatile integration of robust hardware-software architecture for education and testing advanced distribution automation strategies in smart grids. The framework design and its implementation create a realistic environment for testing and design of future automation solutions. The integration of OpenDSS, DSSim-PC, LabView, and JAVA enhance the range of applications by providing a testing platform for centralized and distributed control strategies. The proposed architecture makes this platform highly compatible with other equipment and programming languages. Distributed JAVA clients allow the creation of object-oriented models of elements not defined in OpenDSS nor in DSSim-PC to study novel technologies. Computational performance and scalable test results showed the great potential to implement a smart grid laboratory for research and teaching purposes. Future applications will consider more elements such as distributed generation, storage, electric vehicles and so forth. Distribution automation strategies like fault location, system
978-1-5090-4168-8/16/$31.00 ©2016 IEEE
R EFERENCES [1] R. Podmore and M. R. Robinson, “The Role of Simulators for Smart Grid Development,” IEEE Transactions on Smart Grid, vol. 1, no. 2, pp. 205–212, sep 2010. [2] L. L. Lai, “An overview on smart grid simulator,” in 2012 IEEE Power and Energy Society General Meeting. IEEE, jul 2012, pp. 1–6. [3] K. Anderson, J. Du, A. Narayan, and A. E. Gamal, “GridSpice: A Distributed Simulation Platform for the Smart Grid,” IEEE Transactions on Industrial Informatics, vol. 10, no. 4, pp. 2354–2363, nov 2014. [4] S.-M. Lee, “SGSim: A unified smart grid simulator,” in 2013 IEEE Power & Energy Society General Meeting. IEEE, jul 2013, pp. 1–5. [5] Zhang Haibo and Ge Dandan, “The research and implementation of experimental simulation platform based on RTDS and EMS (OPEN3000),” in IEEE PES Innovative Smart Grid Technologies, vol. 5, no. 4. IEEE, may 2012, pp. 1–4. [6] D. Celeita, M. Hernandez, G. Ramos, N. Penafiel, M. Rangel, and J. D. Bernal, “Implementation of an educational real-time platform for relaying automation on smart grids,” Electric Power Systems Research, vol. 130, pp. 156–166, 2016. [7] I. P. T. o. R.-T. S. o. P. Force and E. Systems, “Real-Time Simulation Technologies for Power Systems Design, Testing, and Analysis,” IEEE Power and Energy Technology Systems Journal, vol. 2, no. 2, pp. 63–73, jun 2015. [8] X. Guillaud, M. O. Faruque, A. Teninge, A. H. Hariri, L. Vanfretti, M. Paolone, V. Dinavahi, P. Mitra, G. Lauss, C. Dufour, P. Forsyth, A. K. Srivastava, K. Strunz, T. Strasser, Davoudi, and Ali, “Applications of Real-Time Simulation Technologies in Power and Energy Systems,” IEEE Power and Energy Technology Systems Journal, vol. PP, no. 99, pp. 1–1, 2015. [9] D. Celeita, S. Zambrano, and G. Ramos, “Fault location framework for distribution systems with DG using DSSim-PC,” in 2014 IEEE PES Transmission & Distribution Conference and Exposition - Latin America (PES T&D-LA). IEEE, Sept 10-13 2014, pp. 1–6. [10] R. P. Foundation, Raspberry Pi, Raspberry Pi Foundation, UK. [Online]. Available: https://www.raspberrypi.org/ [11] K. Rostyslav, S. Tkatchenko, and R. Golovatsyy, “Features home automation system development based raspberry pi using java me sdk,” in Experience of Designing and Application of CAD Systems in Microelectronics (CADSM), 2015 13th International Conference The, Feb 2015, pp. 484–486. [12] V. Ionescu, F. Smaranda, and A.-V. Diaconu, “Control system for video advertising based on raspberry pi,” in Networking in Education and Research, 2013 RoEduNet International Conference 12th Edition, Sept 2013, pp. 1–4. [13] EPRI. (2013) OpenDSS. [Online]. Available: http://sourceforge.net/ projects/electricdss/ [14] F. S. Marc-Andr´e Moffet and David Beauvais, “Review of OpenSource Code Power Grid Simulation Tools for Long-Term Parametic Simulation,” CanmetENERGY, Tech. Rep. 137, July 2011. [15] D. Montenegro. (2013) DSSim-PC, Electrical Distribution System Simulator for PC. Universidad de los Andes. [Online]. Available: https://sourceforge.net/projects/dssimpc/ [16] D. Montenegro, M. Hernandez, and G. A. Ramos, “Real time OpenDSS framework for distribution systems simulation and analysis,” in 2012 Sixth IEEE/PES Transmission and Distribution: Latin America Conference and Exposition (T&D-LA). IEEE, sep 2012, pp. 1–5. [17] IEEE-PES, Distribution Test Feeders, http://ewh.ieee.org/soc/pes/dsacom/testfeeders/ IEEE PES Distribution System Analysis Subcommittee’s Distribution Test Feeder Working Group. [Online]. Available: http://ewh.ieee.org/soc/pes/dsacom/testfeeders/
